Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
4 authors.
Vikram R ShawInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
Jinyoung ByunInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
Younghun HanInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
Christopher I AmosInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA. ciamos@salud.unm.edu.
Funding
Technology to Empower Changes in Health (TECH) Network Participant Technologies CenterU24OD023176 · OD · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2016 to 2022
$204.7M
Precision Medicine Initiative Cohort Program BiobankU24OD023121 · OD · MAYO CLINIC ROCHESTER · PI CEKANOVA, MARIA, CICEK, MINE · 2016 to 2024
$185.5M
Enhancing All of Us Data Resources for Nutrition Precision Health: the All of Us Data and Research CenterU2COD023196 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, DAVID, HARRIS, PAUL A. · 2016 to 2022
$143.7M
Adaptive Platform for Personalized EngagementU24OD023163 · OD · VIGNET, INC. · PI JAIN, PRADUMAN · 2017 to 2020
$102.6M
University of Arizona-Banner Health All of Us Research Program OT2OD026549 · OD · UNIVERSITY OF ARIZONA · PI MORENO, FRANCISCO A, REIMAN, ERIC MICHAEL · 2018 to 2023
$78.9M
California Precision Medicine Research Program ConsortiumOT2OD026552 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANTON-CULVER, HODA A, OHNO-MACHADO, LUCILA · 2018 to 2023
$73.4M
All of Us PennsylvaniaOT2OD026554 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E, VISWESWARAN, SHYAM · 2018 to 2023
$72.1M
New York City Consortium for Precision MedicineOT2OD026556 · OD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BIER, LOUISE E, GHARAVI, ALI G · 2018 to 2023
$67.3M
SouthEast Enrollment Center (SEEC) OT2OD026551 · OD · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI CARRASQUILLO, OLVEEN, COLON, VIVIAN · 2018 to 2023
$62.8M
Southern All of Us NetworkOT2OD026548 · OD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N., KORF, BRUCE R · 2018 to 2023
$60.5M
Illinois Precision Medicine Consortium OT2OD026557 · OD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AHSAN, HABIBUL, ARGOS, MARIA · 2018 to 2023
$60.5M
The New England Precision Medicine Consortium of the All of Us Research ProgramOT2OD026553 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI CLARK, CHERYL RENEE, KARLSON, ELIZABETH W · 2018 to 2023
Smoking may be associated with an increased risk of lung metastasis in cancers of non-lung origin. We leverage survey and electronic health record data from the diverse All of Us Research Program (AoURP) database to investigate whether smoking and smoking-related behaviors increase the risk of lung metastasis in non-lung primary cancers. The results suggest that cigarette use, measured by four continuous variables, does not increase the risk of lung metastasis in seven common cancer types but demonstrates a small significant effect in a cohort including all types of cancer in the database in both univariable and multivariable analyses. An increased odds ratio of electronic smoke use in patients with lung metastasis was seen in multivariable analyses of the all cancer (OR = 1.29, 95% CI = 1.04-1.59, P = 0.02) and liver cancer (OR = 1.57, 95% CI = 1.06-2.28, P = 0.02) groups. After adjusting for estimated cigarette pack years in the multivariable model, the result remained significant for liver cancer (OR = 1.60, 95% CI = 1.02-2.47, P = 0.04) but not the all cancer cohort. These results warrant further inquiry and suggest that smoking and e-cigarettes may be associated with lung metastasis risk in patients with non-lung tumors.
Indexed as
Lung NeoplasmsSmokingAdultAgedFemaleHumansLiver NeoplasmsMaleMiddle AgedNeoplasm MetastasisRisk FactorsUnited States
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Effects of smoking behavior on lung metastasis in the All of Us Research Program. · full record | OpenQuestion